Green concept of neuromarketing based on a systematic review using the bibliometric method

Negin Sangari; Payvand Mirzaeian Khamseh; Shib Sankar Sana; Negin Sangari; Payvand Mirzaeian Khamseh; Shib Sankar Sana

doi:10.3934/GF.2023016

Green Finance

2023, Volume 5, Issue 3: 392-430. doi: 10.3934/GF.2023016

Previous Article Next Article

Review Special Issues

Green concept of neuromarketing based on a systematic review using the bibliometric method

1.
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
2.
Department of Mathematics, Kishore Bharati Bhagini Nivedita College, Ramkrishna Sarani, Behala, Kolkata, 700060, India

Received: 02 August 2023 Revised: 25 August 2023 Accepted: 30 August 2023 Published: 18 September 2023
JEL Codes: G41, M31, Z33

Unlike traditional marketing methods, neuromarketing has shown new insights and higher prediction accuracy. This research uses the bibliometric method to analyze the objectives like the analysis and integration of the green concept of neuromarketing, recognition of the useful authors, the years of publication of documents, authoritative journals that publish articles in this field and keywords around the concept of neuromarketing. The tools presented in neuromarketing expand and improve the perception of the enthusiasts and researchers in this field, and it compares the results obtained from different approaches. From the methodological point of view, this research is qualitative and based on Iden et al.'s (2017) model, consisting of four steps of planning, selecting, extracting and implementing and combining it with setting of Silva's (2015) articles in the form of a review. A bibliometric system is implemented, and VOS viewer software was used to analyze the results.

The findings are presented in two phases. In the first phase, the performance analysis, the share of the annual production of neuromarketing documents, the percentage of the production of authoritative quarterly journals of this field, the share of the output of related subject areas, the share of the countries' published articles and the share of the documents by productive authors were identified and studied. Also, knowledge maps were drawn in the second phase, and 17 clusters are found, including 109 items and 131 keywords. The theoretical contribution of this article consists of the field of green neuromarketing, which is categorized into four clusters with themes of sustainability and green consumption. The results of this study were obtained based on the framework of theory, context, method, antecedents, decisions, and outcomes. All the keywords related to neuromarketing were categorized from the analysis of the previous articles and its features were studied in the proposed model.

Keywords:

Citation: Negin Sangari, Payvand Mirzaeian Khamseh, Shib Sankar Sana. Green concept of neuromarketing based on a systematic review using the bibliometric method[J]. Green Finance, 2023, 5(3): 392-430. doi: 10.3934/GF.2023016

Related Papers:

[1]	Ivana Sirangelo, Margherita Borriello, Gaetano Irace, Clara Iannuzzi . Intrinsic blue-green fluorescence in amyloyd fibrils. AIMS Biophysics, 2018, 5(2): 155-165. doi: 10.3934/biophy.2018.2.155
[2]	Jonathan Trauth, Johannes Scheffer, Sophia Hasenjäger, Christof Taxis . Strategies to investigate protein turnover with fluorescent protein reporters in eukaryotic organisms. AIMS Biophysics, 2020, 7(2): 90-118. doi: 10.3934/biophy.2020008
[3]	Marek K. Korzeniowski, Barbara Baird, David Holowka . STIM1 activation is regulated by a 14 amino acid sequence adjacent to the CRAC activation domain. AIMS Biophysics, 2016, 3(1): 99-118. doi: 10.3934/biophy.2016.1.99
[4]	David E. Shoup . Diffusion-controlled reaction rates for clusters of binding sites on a cell. AIMS Biophysics, 2016, 3(4): 522-528. doi: 10.3934/biophy.2016.4.522
[5]	Vladimir B. Teif, Andrey G. Cherstvy . Chromatin and epigenetics: current biophysical views. AIMS Biophysics, 2016, 3(1): 88-98. doi: 10.3934/biophy.2016.1.88
[6]	Megan A. Ahern, Claudine P. Black, Gregory J. Seedorf, Christopher D. Baker, Douglas P. Shepherd . Hyperoxia impairs pro-angiogenic RNA production in preterm endothelial colony-forming cells. AIMS Biophysics, 2017, 4(2): 284-297. doi: 10.3934/biophy.2017.2.284
[7]	Ateeq Al-Zahrani, Natasha Cant, Vassilis Kargas, Tracy Rimington, Luba Aleksandrov, John R. Riordan, Robert C. Ford . Structure of the cystic fibrosis transmembrane conductance regulator in the inward-facing conformation revealed by single particle electron microscopy. AIMS Biophysics, 2015, 2(2): 131-152. doi: 10.3934/biophy.2015.2.131
[8]	Larisa A. Krasnobaeva, Ludmila V. Yakushevich . DNA kinks behavior in the potential pit-trap. AIMS Biophysics, 2022, 9(2): 130-146. doi: 10.3934/biophy.2022012
[9]	Yu-Ting Huang, Hui-Fen Liao, Shun-Li Wang, Shan-Yang Lin . Glycation and secondary conformational changes of human serum albumin: study of the FTIR spectroscopic curve-fitting technique. AIMS Biophysics, 2016, 3(2): 247-260. doi: 10.3934/biophy.2016.2.247
[10]	Larisa I. Fedoreyeva, Tatiana A. Dilovarova, Boris F. Vanyushin, Inna A. Chaban, Neonila V. Kononenko . Regulation of gene expression in Nicotiana tabacum seedlings by the MKASAA peptide through DNA methylation via the RdDM pathway. AIMS Biophysics, 2022, 9(2): 113-129. doi: 10.3934/biophy.2022011

Abstract

1. Introduction

Some proteins are considered cytotoxic because their overexpression causes cellular growth defects. However, toxicity is a relative quantity that can only be defined by comparison. The expression limit of a protein, namely, the protein expression level that results in growth defects, may be considered as an index for protein cytotoxicity [1]. In the model eukaryote Saccharomyces cerevisiae, fluorescent proteins and glycolytic enzymes have the highest expression limits, which means that they are the least cytotoxic in this organism [2], while reports suggest that fluorescent proteins could be cytotoxic in other cells [3],[4]. The cytotoxicity of fluorescent proteins can be increased (i.e., their expression limits are decreased) by adding localization signals or mutations that cause misfolding [5],[6]. The mechanism determining the expression limit of a non-toxic protein is recognized as the “protein burden/cost effect,” in which cellular protein production resources are overloaded by extreme overexpression of the protein [1],[7]. In S. cerevisiae, the limit that triggers protein burden is about 15% of the total protein content [2].

The nucleotide sequence that corresponds to the N-terminal of the protein strongly affects the expression level of the protein by modulating the efficiency of translation [8]. The N-terminal amino acid sequences of proteins also affect the stability and thus the expression levels of the protein [9]. N-terminal amino acid sequences also affect the cytotoxicity of proteins because they often transmit localization signals [5]. Although there are several systematic analyses revealing the relationship between amino acid sequences and expression levels [9],[10], predicting the effect of the N-terminal sequence on the expression level and cytotoxicity of the protein remains challenging.

In this study, the effects of the N-terminal sequence on the expression level and cytotoxicity of green fluorescent protein (GFP) were systematically analyzed. Random 30 nucleotides (i.e., ten amino acids) were inserted into the coding sequence for the N-terminal region of GFP. This length was chosen because peptide sequences shorter than ten amino acid length could be linkers connecting different proteins [11], protein localization signals [12]–[14], and protein tags [15],[16] used for protein engineering. GFP consists of a beta-barrel structure as its main tertiary structure [17], and additional N-terminal sequences should be located outside of the main structure.

The effects of the sequence on the expression level and GFP cytotoxicity were assessed using the expression per gene (EPG) and gene copy number limit (CNL) relationship. Several sequences that would decrease the expression level or increase the cytotoxicity were isolated. The lower expression level was associated with codon optimality, and cytotoxicity was increased by the existence of hydrophobic or cysteine residues.

2. Materials and Method

2.1. Strain, plasmid, media, and growth conditions

The S. cerevisiae strain BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) [18] was used in all experiments. pTOW40836-TDH3pro-yEGFP (Figure 1A) [19] was used as the base plasmid. The cultivation and transformation of yeast were performed as previously described [20]. Saccharomyces cerevisiae cells were grown in synthetic complete (SC) medium without uracil (−Ura) or without leucine and uracil (−Leu/Ura) conditions. The SC medium was made using the yeast nitrogen base (4027–512, MP Biomedicals), glucose (16806–25, Nacalai tesque), the –His/–Leu/–Ura drop-out mix (630423, Clontech), histidine (18116–92, Nacalai tesque), and leucine (20327–75, Nacalai tesque).

Figure 1. Experimental framework isolating N10 sequences affecting the expression level and cytotoxicity of N10-GFPs. (A) Plasmid construct used in this study. Thirty random nucleotides (ten amino acids) were inserted into the N-terminal of yEGFP. Constructed N10-GFPs were expressed under the control of the TDH3 promoter (TDH3pro). The base plasmid used was a multicopy plasmid pTOW40836, whose copy number may be controlled by culture conditions. (B) The experimental procedure of gTOW, with the principle of how copy number limit (CNL) of the target gene is determined by the gTOW. The detail of the figure is explained in the main text.

DownLoad: Full-Size Img PowerPoint

2.2. Preparation of yeast strains with N10-GFP plasmids

Two DNA fragments were amplified with the primers OHM1002 (5′-CATTTTGTTTGTTTATGTGT-3′)–OSBI851 (5′-ATGAGTATTCAACATTTCCG-3′) and OHM600 (5′-ATGTCTAAAGGTGAAGAATT-3′)–OSBI850 (5′-TTACCAATGCTTAATCAGTG-3′) using pTOW40836-TDH3pro-yEGFP as a template. The N10 DNA fragment was prepared via single-cycle amplification of the template DNA (OHM1000: 5′-TAAACACACATAAACAAACAAAATGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTCTAAAGGTGAAGAATTATTCACTG-3′) using the primer OHM1001 (5′-CAGTGAATAATTCTTCACCTTTAGA-3′). The DNA fragments were amplified using KOD-Plus-NEO (KOD-401, TOYOBO). All three DNA fragments were simultaneously introduced into BY4741 cells to join them, and the transformants were cultured on an SC–Ura plate. The N10-GFP plasmids were constructed by homologous recombination-based cloning in vivo [21]. Randomly-chosen 94 colonies of the transformants were picked-up and used for further analysis.

2.3. Measurement of growth rate, GFP fluorescence, and plasmid copy number

Each yeast strain was cultivated in 200 µL SC–Ura media in each well of a 96-well plate (161093, Thermo) at 30 °C for overnight to prepare the preculture. The precultured cells (5 µL each) were transferred into 200 µL of SC–Ura or SC–Leu/Ura medium in 96-well plates, and grown at 30 °C without agitation. The cell density (absorbance 595 nm) and GFP fluorescence (485 nm excitation – 535 emission) were measured every 30 min for 50 hours using the Infinite F200 microplate reader (TECAN). The maximum growth rate (min ⁻¹) during the 270 min interval was calculated from the cell density measurements, and the maximum GFP fluorescence (AU) was obtained from the GFP fluorescence measurements. After cell cultivation, cells were collected, and the plasmid copy number in the cell (copies per cell) was measured via real-time PCR as described previously [22]. The expression per gene (EPG, AU) of each N10-GFP was calculated by dividing the maximum GFP fluorescence in SC–Ura by the plasmid copy number in SC–Ura. The plasmid copy number in SC–Leu/Ura was considered as the copy number limit (CNL) of each N10-GFP as the reason described in Results.

2.4. Bioinformatic analysis

The folding energy of mRNA ΔG (kcal/mol) containing the N10 sequence (from −4 nt to +37 nt) was calculated using the mfold web server (http://unafold.rna.albany.edu/?q=mfold). Codon optimality was calculated as the tRNA adaptation index (tAI) using the stALcalc (http://tau-tai.azurewebsites.net/). The existence of a localization signal was predicted using DeepLoc-1.0 (http://www.cbs.dtu.dk/services/DeepLoc/). The GRAVY value was calculated using the GRAVY calculator (http://www.gravy-calculator.de/). The distribution of expected GRAVY values in the 10 amino acid sequences translated from random 30 nucleotide sequences (mean = −0.254 and standard deviation = 0.950) were estimated from the 100,000 randomly generated sequences. The existence of the transmembrane domain (TM) and secondary structure in the N-terminal 30 a.a. were predicted using TMHMM server v. 2.0 (http://www.cbs.dtu.dk/services/TMHMM/) and Jpred4 (http://www.compbio.dundee.ac.uk/jpred/), respectively. The statistical analyses were performed using Microsoft Excel (version 16.36).

3. Results

3.1. Isolation of N-terminal sequences that modulate the expression level and GFP cytotoxicity

Figure 1A shows the plasmid used to assess the effects of the N-terminal sequences on the expression level and GFP cytotoxicity. A yeast codon-optimized enhanced GFP (yEGFP3) [23] was the GFP used. After the N-terminal methionine, random 30 nucleotides encoding 10 amino acids (N10 sequences) were inserted by homologous recombination-based cloning in S. cerevisiae. The resulting N10-GFPs were expressed under the control of the strong TDH3 promoter (TDH3pro). pTOW40836 was used as a backbone plasmid. The copy number of pTOW40836 in S. cerevisiae cells cultured without leucine and uracil (−Leu/Ura) reflects the CNL of the gene. Under such conditions, the overexpressed gene product inhibits the growth of the cell owing to the genetic tug-of-war (gTOW) effect [22],[24].

The principle of how CNL is determined by the gTOW is explained as follows (Figure 1B). A target gene is cloned into a plasmid for gTOW (Step 1 in Figure 1B), and is used to transform an ura3Δ leu2Δ strain (Step 2 in Figure 1B). In this study, we used TDH3pro_N10-GFP, pTOW40836, and BY4741 as the target, the plasmid for gTOW, and the host strain, respectively. Because of 2µ ORI on the plasmid, the copy number of the plasmid becomes multicopy and diverse among the population. When the cells are transferred into the –Leu/Ura media, the plasmid copy number becomes >100 copies per cell (Step 3 in Figure 1B). This is performed due to leu2-89 on the plasmid, which is an allele of LEU2 harboring a large deletion within its promoter and thus weakly expressed. A higher copy number of leu2-89 is required to fulfill the demand of Leu2 protein to synthesize an essential nutrient leucine (red line in Figure 1B). However, if the target gene has the expression limit to cause cellular defects, the gene copy number and thus the plasmid copy number should be less than the limit, because overexpression of the target beyond the limit causes growth defects (blue line in Figure 1B). The resulting tug-of-war of selection biases between leu2-89 and the target gene (blue and red arrows in Figure 1B) determines the plasmid's copy number, which is determined by the real-time PCR (Step4 in Figure 1B). Because the bias of leu2-89 is always the same, the copy number should reflect the copy number limit (CNL) of the target gene.

Our framework for identifying N10 sequences affecting the expression level and GFP cytotoxicity is explained as follows. The GFP EPG and CNL of the gene should be inversely proportional because the expression limit of GFP (EPG × CNL) is constant; for example, if there are three genes encoding the same GFP, but their EPGs are low, medium, and high, their CNLs should be high, medium, and low, respectively (Figure 2A). The inversely proportional curve is considered as the frontier line in the EGP–CNL space made by the expression limit of GFP (Figure 2B). If an N10 sequence simply changes the EPG of the N10-GFPs, but not its cytotoxicity and expression limit, the EPG–CNL relationship should be on the same frontier line. Conversely, if an N10 sequence increases the cytotoxicity, thus reducing the expression limit of N10-GFPs, the EPG–CNL relationship should shift to the left (Figure 2B, white arrow). Therefore, N10 sequences, which may alter the expression level and GFP cytotoxicity, can be systematically isolated by analyzing the EPG–CNL relationship.

Figure 2. The GFP expression level per gene (EPG) and gene copy number limit (CNL) are inversely proportional. See the main text for the details.

DownLoad: Full-Size Img PowerPoint

Overall, 94 yeast colonies containing the N10-GFP plasmid were randomly isolated. Then, their growth rates, level of fluorescence, and plasmid copy numbers without uracil (−Ura) and −Leu/Ura conditions (Table S1) were measured. If analyzed N10-GFPs are under the gTOW regime described above, the growth rates and copy numbers in –Leu/Ura conditions should be on the linear red line created by the relationship between the growth rate and copy number of leu2-89 (Figure 1B). The relationship between the growth rates and copy numbers among the 94 isolates was r = 0.82 (Table S1), suggesting that they are under the gTOW regime, and thus the plasmid copy number under −Leu/Ura conditions was considered as the gene CNL. The GFP EPG was calculated by dividing the level of GFP fluorescence by the plasmid copy number. To calculate the EPG, the level of GFP fluorescence and plasmid copy number obtained under −Ura conditions were used, wherein GFP overexpression produces less growth defects. As shown in Figure 3A, the 94 isolates exhibited various EPG–CNL relationships, which indicates that the N10 sequences modulated the expression levels and toxicities of GFP. Importantly, the EPG–CNL relationships created a frontier line (dotted line in Figure 3A) as it is theoretically predicted in Figure 2B. Most of the N10-GFPs were located around the frontier line, suggesting that most N10 sequences just modulate the expression level of GFP but not the cytotoxicity. Sixteen representative isolates were selected (Figure 3A), and the N10 sequences of each were determined. Seven isolates (#6, #44, #2, #20, #90, #1, and #85) were on the frontier line, but their locations were different, which suggests that their N10 sequences modulated the expression levels of GFP. Four isolates (#15, #75, #80, and #82) were located inside the frontier line, which suggests that their N10 sequences were cytotoxic. Three toxic non-fluorescent isolates (#51, #52, and #64) and two non-toxic non-fluorescent isolates (#55 and #74) were also analyzed.

Figure 3. EPG–CNL relationships of isolated N10-GFPs. (A) Scatterplot between EPGs and CNLs of isolated N10-GFPs. Colored and numbered isolates were studied further. The dotted line indicates the predicted protein burden frontier line. (B) EPGs and CNLs of representative N10-GFP isolates. For each sample, the data from a single experiment are shown.

DownLoad: Full-Size Img PowerPoint

Table 1 presents the sequencing results. First, toxic non-fluorescent isolates (#51, #52, and #64), but not N10-GFPs, contained the TDH3 gene. Therefore, the plasmids were probably constructed through homologous recombination between the base plasmid and TDH3 within the S. cerevisiae genome. Because Tdh3 has a lower expression limit than GFP [19], isolates expressing Tdh3 should have exhibited EPG–CNL relationships that differed from those of GFP. Second, non-toxic non-fluorescent isolates (#55 and #74) had nonsense codons within their N10 sequences, indicating that the genes do not encode any proteins. Third, isolates showing the highest EPGs (#6 and #44) did not contain the N10 sequence but rather GFP alone; these isolates were probably constructed by self-ligation of the base plasmid. Because #6 and #44 are located on the top of the frontier line, inserting an N10 sequence seemed to reduce the expression level or increase the GFP cytotoxicity. The other nine isolates contained various N10 sequences; among these, five (#2, #20, #90, #1, and #85) decreased the expression level, whereas four (#15, #75, #70, and #82) increased cytotoxicity as shown in Figure 3A. Figure 3B shows the CNLs and EPGs of the representative isolates.

Table 1. Isolated N10 sequences.

Clone #	Category	Nucleotide sequence	Amino acid sequence
#6	High nontoxic	without N10 sequence
#44	High nontoxic	without N10 sequence
#2	middle nontoxic	ATGAGCATGATACGGGAGGTTGATGAATGTTTA	MSMIREVDECL
#20	middle nontoxic	ATG TGTACTGGACTCGATGTTGGCCCCGTCCAA	MCTGLDVGPVQ
#90	middle nontoxic	ATGTCCGCAGCTGCGATCGGGTGCGGGTATGTA	MSAAAIGCGYV
#1	Low nontoxic	ATGGGATGGCTGCCGAAAGGGTTCTTAGAAGAG	MGWLPKGFLEE
#85	Low nontoxic	ATGGTGGTGGTGGGTTGGCAGCCCCCGGGTGGG	MVVVGWQPPGG
#15	toxic	ATGTGCTTCGGGGTAATGAGGGGTATCTGGCTG	MCFGVMRGIWL
#75	toxic	ATGTGCACTGAGCTGTGTTTTCTTCTCGGTGTC	MCTELCFLLGV
#80	toxic	ATGCCGGTGATTGGGTATTTTGATGGCTTGTTA	MPVIGYFDGLL
#82	toxic	ATGGATCCATTGTGCCAGTTGATGGGTGTGCTA	MDPLCQLMGVL
#51	No expression, toxic	without N10 sequence, TDH3
#52	no expression, toxic	without N10 sequence, TDH3
#64	no expression, toxic	without N10 sequence, TDH3
#55	no expression, nontoxic	ATGTCCTCTGCTCATTCGTTGCCGGTATAGGGT	MSSAHSLPV*G
#74	no expression, nontoxic	ATGTAGAAATTTACGGTTGCGATCGCCAGAGTG	M*KFTVAIARV

| Show Table

DownLoad: CSV

3.2. Codon optimality of the N10 sequence affects the expression level

To reveal the sequence property that determines the expression level of N10-GFPs, the mRNA folding energy [25] and codon optimality (tRNA adaptation index; tAI) [26] were calculated (Table S2). The existence of localization signals [27] was also predicted. Although no significantly associated values with the expression level and cytotoxicity were noted, the tAIs of isolated N10 sequences were found to be lower than those of GFP (0.320). Their codon usage was thus optimized to that of the highly expressed TDH3. Codon optimization altered the EPG–CNL relationship profiles of N10-GFPs (Figure 4). Most importantly, the optimization increased the EPGs of isolates with low expression levels of N10-GFPs (#1 and #85), which suggests that codon optimality is responsible for their low EPGs. Thus, codon optimality of the N10 sequences seemed to be the most important contributing factor to the expression levels of N10-GFPs.

Figure 4. Effect of codon optimization on the EPGs and CNLs of N10-GFPs. Scatterplot between EPGs and CNLs of original and codon-optimized N10-GFPs. Arrows indicate the changes by codon optimization. The dotted line indicates the predicted protein burden frontier line the same as shown in Figure 3A. Averages of four biological replicates are shown.

DownLoad: Full-Size Img PowerPoint

3.3. Existence of hydrophobic and cysteine residues in the N10 sequence might increase N10-GFP cytotoxicity

Next, the relationship between the amino acid composition in the N10 sequence and N10-GFP cytotoxicity was analyzed. First, the grand average of hydropathy (GRAVY) value of the amino acid sequence [28] (Table S2) was calculated, which has a positive relationship with hydrophobicity. The toxic N10 sequences had significantly higher GRAVY values than those of the non-toxic N10 sequences (p = 0.028, Student t-test, Figure 5A). The probability of obtaining four N10 sequences with GRAVY values of >0.696 is <0.0006, which suggests that hydrophobic residues contribute to the cytotoxicity of the N10 sequences. Next, the frequency of each amino acid in the four toxic N10 sequences (#15, #75, #80, and #82) was calculated and compared with the expected frequencies in the codon table (Figure 5B). The quantities of cysteine, methionine, and tryptophan residues were over two-fold of the expected frequencies. The p-value of the appearance of cysteine was 0.006 according to the results of the chi-square test, which was slightly insignificant under the false discovery rate <0.05 (p < 0.003). Therefore, we concluded that cysteine residues were over-represented in toxic N10-GFPs. Structural analyses of the N-terminal 30 a.a. containing N10 sequences and the N-terminal of GFP were also performed (Table S2). A transmembrane domain prediction by TMHMM (v2.0) [29] did not give any transmembrane domain in the sequences, and a secondary structure prediction by Jpred4 [30] gave some structures, but there was no structural consistency within the toxic and non-toxic N10 sequences.

Figure 5. Characteristics of toxic N10 sequences. (A) GRAVY values of the non-toxic and toxic N10 sequences. The p-value of the Student t-test between the GRAVY scores of the non-toxic and toxic N10 sequences is presented. (B) Frequency of appearance of each amino acid in the toxic N10 sequences. The frequency was calculated as the appeared over the expected frequency. The p-value of the chi-square test is shown.

DownLoad: Full-Size Img PowerPoint

4. Discussions

The nucleotide sequence encoding the N-terminal of a protein plays a substantial role in protein production because it is where ribosomes are assembled and translation is initiated. The secondary structure of mRNA and codon optimality affect the initiation of translation [8]. In this study, it was confirmed that codon optimality was a major factor in reducing the expression level of GFP, although mRNA stability did not affect the expression level. A question that was raised during this study focused on the mechanism causing cytotoxicity of N10 sequences. A possible explanation for this phenomenon is the artificial creation of localization signals; this occurs because the addition of localization signals at the N-terminal of GFP increases its cytotoxicity [5]. However, three out of the four toxic N10 sequences did not have any signature localization signal. Moreover, no localization of toxic N10-GFPs (data not shown) was observed. Agammaegative sequences also increase the cytotoxicity of proteins [31]; however, no agammaegation of toxic N10-GFPs (data not shown) was noted. Although a small subset of toxic N10 sequences was analyzed here, they showed statistically significant concentration of hydrophobic residues and a preferential appearance of cysteine residues. Overexpression of cysteine-containing glycolytic enzymes constitutes the agammaegates connected by the S–S bond [2],[32], and removal of cysteine from Tpi1 increases its expression limit [2]. Therefore, the existence of unimportant cysteines might increase the cytotoxicity of highly expressed proteins regardless of their positions. Hydrophobic residues seemed to increase the GFP cytotoxicity. This may be owing to non-specific hydrophobic interactions, and therefore, they might not be preferred in the outside of the main tertiary structure of proteins. Another possible mechanism to cause cytotoxicity is the perturbation of translation. Sequences rich in proline and positively-charged residues cause slowdown in translational elongation [33]. However, this might not be the case in this analysis because any enrichment of proline or positively-charged residues (lysine or arginine) was observed in the toxic N10 sequences (Figure 5B).

References

[1]	Adebayo TS (2023) Trade‐off between environmental sustainability and economic growth through coal consumption and natural resources exploitation in China: New policy insights from wavelet local multiple correlation. Geol J 58: 1384–1400. https://doi.org/10.1002/gj.4664 doi: 10.1002/gj.4664
[2]	Adil S, Lacoste-Badie S, Droulers O (2018) Face presence and gaze direction in print advertisements: how they influence consumer responses—an eye-tracking study. J Advertising Res 58: 443–455. https://doi.org/10.2501/JAR-2018-004 doi: 10.2501/JAR-2018-004
[3]	Akhter S, Pauyo T, Khan M (2019) What is the difference between a systematic review and a meta-analysis? Basic methods handbook for clinical orthopaedic research: a practical guide and case-based research approach, 331–342. https://doi.org/10.1007/978-3-662-58254-1_37 doi: 10.1007/978-3-662-58254-1_37
[4]	Aly E, Elsawah S, Ryan MJ (2022) A review and catalogue to the use of models in enabling the achievement of sustainable development goals (SDG). J Clean Prod 340: 130803. https://doi.org/10.1016/j.jclepro.2022.130803 doi: 10.1016/j.jclepro.2022.130803
[5]	Bakalash T, Riemer H (2013) Exploring ad-elicited emotional arousal and memory for the ad using fMRI. J Advert 42: 275–291. https://doi.org/10.1080/00913367.2013.768065 doi: 10.1080/00913367.2013.768065
[6]	Baldo D, Viswanathan VS, Timpone, RJ, et al. (2022) The heart, brain, and body of marketing: complementary roles of neurophysiological measures in tracking emotions, memory, and ad effectiveness. Psyc Mark 39: 1979–1991. https://doi.org/10.1002/mar.21697 doi: 10.1002/mar.21697
[7]	Bandari R, Moallemi EA, Lester RE, et al. (2022) Prioritising Sustainable Development Goals, characterising interactions, and identifying solutions for local sustainability. Env Sci Pol 127: 325–336. https://doi.org/10.1016/j.envsci.2021.09.016 doi: 10.1016/j.envsci.2021.09.016
[8]	Bergamaschi M, Randerson K (2016) The futures of family businesses and the development of corporate social responsibility. Futures 75: 54–65. https://doi.org/10.1016/j.futures.2015.10.006 doi: 10.1016/j.futures.2015.10.006
[9]	Berkman ET, Falk EB (2013) Beyond brain mapping: Using neural measures to predict real-world outcomes. Curr Direc Psych Sci 22: 45–50. https://doi.org/10.1177/0963721412469394 doi: 10.1177/0963721412469394
[10]	Biglari S, Beiglary S, Arthanari T (2022) Achieving sustainable development goals: Fact or Fiction?. J Clean Prod 332: 130032. https://doi.org/10.1016/j.jclepro.2021.130032 doi: 10.1016/j.jclepro.2021.130032
[11]	Cahn BR, Delorme A, Polich J (2013) Occipital gamma activation during vipassana meditation. Cogn Proces 14: 393–406. https://doi.org/10.1007/s10339-009-0352-1 doi: 10.1007/s10339-009-0352-1
[12]	Cakir MP, Çakar T, Girisken Y, et al. (2018) An investigation of the neural correlates of purchase behavior through fNIRS. Eur J Mark 52: 224–243. https://doi.org/10.1108/EJM-12-2016-0864 doi: 10.1108/EJM-12-2016-0864
[13]	Carroll AB (2016) Carroll's pyramid of CSR: Taking another look. Int J Corp Soc Res 1: 1–8. https://doi.org/10.1186/s40991-016-0004-6 doi: 10.1186/s40991-016-0004-6
[14]	Chang L, Tsao DY, Liu T (2021) Distributed representation of visual objects by single neurons in macaque visual cortex. Nat Commun 12: 1–13. https://doi.org/10.1523/JNEUROSCI.1958-14.2015 doi: 10.1038/s41467-020-20314-w
[15]	Chen JE, Glover GH (2021) Advances in BOLD fMRI methodology. Tren Cogn Sci 25: 511–527. https://doi.org/10.1016/j.tics.2017.09.010 doi: 10.1016/j.tics.2017.09.010
[16]	Cheng Y, Yang CY, Lin CP, et al. (2008) The perception of pain in others suppresses somatosensory oscillations: a magnetoencephalography study. Neuroimage 40: 1833–1840. https://doi.org/10.1016/j.neuroimage.2008.01.064 doi: 10.1016/j.neuroimage.2008.01.064
[17]	Ćirović M, Dimitriadis N, Janić M, et al. (2022) More than words: Rethinking sustainability communications through neuroscientific methods. J Cons Behav. https://doi.org/10.1002/cb.2125 doi: 10.1002/cb.2125
[18]	Clark KR, Leslie KR, Garcia-Garcia M, et al. (2018) How advertisers can keep mobile users engaged and reduce video-ad blocking: Best practices for video-ad placement and delivery based on consumer neuroscience measures. J Adv Res 58: 311–325. https://doi.org/10.2501/JAR-2018-036 doi: 10.2501/JAR-2018-036
[19]	Daugherty T, Hoffman E, Kennedy K, et al. (2018) Measuring consumer neural activation to differentiate cognitive processing of advertising: Revisiting Krugman. Eur J Mark 52: 182–198. https://doi.org/10.1108/EJM-10-2017-0657 doi: 10.1108/EJM-10-2017-0657
[20]	de Oliveira JHC, Giraldi JDME (2017) What is neuromarketing? A proposal for a broader and more accurate definition. Glob Bus Manage Res 9: 19–29. Available from: http://gbmrjournal.com/pdf/vol.%209%20no.%202/V9N2-2.pdf
[21]	de Oliveira L, Prado JW (2021) Emotion and advertising effectiveness: A facial analysis study. Int J Adv 40: 455–472. Available from: http://www.gbmrjournal.com/pdf/vol.%209%20no.%202/V9N2-2.pdf
[22]	De Vries EL, Fennis BM, Bijmolt TH, et al. (2018) Friends with benefits: Behavioral and fMRI studies on the effect of friendship reminders on self-control for compulsive and non-compulsive buyers. Int J Res Mark 35: 336–358. https://doi.org/10.1016/j.ijresmar.2017.12.004 doi: 10.1016/j.ijresmar.2017.12.004
[23]	Donthu N, Kumar S, Pandey N, et al. (2021) Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. J Bus Res 135: 758–773. https://doi.org/10.1016/j.jbusres.2021.07.015 doi: 10.1016/j.jbusres.2021.07.015
[24]	Farooq O, Rupp DE, Farooq M (2017) The multiple pathways through which internal and external corporate social responsibility influence organizational identification and multifoci outcomes: The moderating role of cultural and social orientations. Acad Manage J 60: 954–985. https://doi.org/10.5465/amj.2014.0849 doi: 10.5465/amj.2014.0849
[25]	Figner B, Murphy RO, Siegel P (2019) Measuring electrodermal activity and its applications in judgment and decision-making research, In: A Handbook of Process Tracing Methods, Routledge, 161–183. https://doi.org/10.4324/9781315160559-12
[26]	Foroudi P, Akarsu TN, Marvi R, et al. (2021) Intellectual evolution of social innovation: A bibliometric analysis and avenues for future research trends. Ind Mark Manage 93: 446–465. https://doi.org/10.1016/j.indmarman.2020.03.026 doi: 10.1016/j.indmarman.2020.03.026
[27]	Fortunato VCR, Giraldi JDME, de Oliveira JHC (2014) A review of studies on neuromarketing: Practical results, techniques, contributions and limitations. J Manage Res 6: 201–220. https://doi.org/10.5296/jmr.v6i2.5446 doi: 10.5296/jmr.v6i2.5446
[28]	Frederick DP (2022) Recent Trends in Neuro marketing–An Exploratory Study. Int J Case Stud Bus IT Edu. (IJCSBE), 6: 38–60. https://doi.org/10.47992/IJCSBE.2581.6942.0148 doi: 10.47992/IJCSBE.2581.6942.0148
[29]	Garczarek-Bąk U, Szymkowiak A, Gaczek P, et al. (2021) A comparative analysis of neuromarketing methods for brand purchasing predictions among young adults. J Brand Manage 28: 171–185. https://doi.org/10.1057/s41262-020-00221-7 doi: 10.1057/s41262-020-00221-7
[30]	Gazzaniga MS, Ivry RB, Mangun GR (2021) Cognitive neuroscience: The Biology of the Mind (5th ed.), W. W. Norton & Company. Available from: https://www.amazon.com/Cognitive-Neuroscience-Biology-Mind-Fifth/dp/0393603172
[31]	Ghaffar A, Islam T (2023) Factors leading to sustainable consumption behavior: an empirical investigation among millennial consumers. Kyber. https://doi.org/10.1108/K-12-2022-1675 doi: 10.1108/K-12-2022-1675
[32]	Gómez-Carmona D, Marín-Dueñas PP, Tenorio RC, et al. (2022) Environmental concern as a moderator of information processing: A fMRI study. J Clean Prod 369: 133306. https://doi.org/10.1016/j.jclepro.2022.133306 doi: 10.1016/j.jclepro.2022.133306
[33]	Gountas J, Gountas S, Ciorciari J, et al. (2019) Looking beyond traditional measures of advertising impact: Using neuroscientific methods to evaluate social marketing messages. J Bus Res105: 121–135. https://doi.org/10.1016/j.jbusres.2019.07.011 doi: 10.1016/j.jbusres.2019.07.011
[34]	Hakim A, Klorfeld S, Sela T, et al. (2021) Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning. Int J Res Mark 38: 770–791. https://doi.org/10.1016/j.ijresmar.2020.10.005 doi: 10.1016/j.ijresmar.2020.10.005
[35]	Hakimi N, Shahbakhti M, Sappia S, et al. (2022) Estimation of respiratory rate from functional near-infrared spectroscopy (fNIRS): A new perspective on respiratory interference. Bios 12: 1170. https://doi.org/10.3390/bios12121170 doi: 10.3390/bios12121170
[36]	Halkiopoulos C, Antonopoulou H, Gkintoni E, et al. (2022) Neuromarketing as an indicator of cognitive consumer behavior in decision-making process of tourism destination—An overview, In: Transcending Borders in Tourism Through Innovation and Cultural Heritage: 8th International Conference, IACuDiT, Hydra, Greece, 2021, Cham: Springer International Publishing, 679–697. https://doi.org/10.1007/978-3-030-92491-1_41
[37]	Hamelin N, Thaichon P, Abraham C, et al. (2020) Story telling, the scale of persuasion and retention: A neuromarketing approach. J Retail Cons Serv 55: 102099. https://doi.org/10.1016/j.jretconser.2020.102099 doi: 10.1016/j.jretconser.2020.102099
[38]	Holmqvist K, Örbom SL, Hooge IT, et al. (2023) Eye tracking: empirical foundations for a minimal reporting guideline. Behav Res Method 55: 364–416. https://doi.org/10.3758/s13428-021-01762-8 doi: 10.3758/s13428-021-01762-8
[39]	Hsu M (2017) Neuromarketing: inside the mind of the consumer. Calif Manage Rev 59: 5–22. https://doi.org/10.1177/0008125617720208 doi: 10.1177/0008125617720208
[40]	Hsu MYT, Cheng JMS (2018) fMRI neuromarketing and consumer learning theory: word-of-mouth effectiveness after product harm crisis. Eur J Mark 52: 199–223. https://doi.org/10.1108/EJM-12-2016-0866 doi: 10.1108/EJM-12-2016-0866
[41]	Iden J, Methlie LB, Christensen GE (2017) The Nature of Strategic Foresight Research: A Systematic Literature Review. Technol Forecast Social Change 116: 87–97. https://doi.org/10.1016/j.techfore.2006.10.001 doi: 10.1016/j.techfore.2016.11.002
[42]	Jai TM, Fang D, Bao FS, et al. (2021) Seeing it is like touching it: Unraveling the effective product presentations on online apparel purchase decisions and brain activity (An fMRI Study). J Int Mark 53: 66–79. https://doi.org/10.1016/j.intmar.2020.04.005 doi: 10.1016/j.intmar.2020.04.005
[43]	Kansra P, Oberoi S, Gupta SL, et al. (2022) Factors limiting the application of consumer neuroscience: A systematic review. J Cons Behav. https://doi.org/10.1002/cb.2131 doi: 10.1002/cb.2131
[44]	Khashei V, Harandi AO (2015) Explaining strategic control model in weight industry: discourse exploration using grounded theory strategy. J Strat Manage Stud 6: 81–80.
[45]	Keles HO, Karakulak EZ, Hanoglu L, et al. (2022) Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy. Front Hum Neur 16: 1061668. https://doi.org/10.3389/fnhum.2022.1061668 doi: 10.3389/fnhum.2022.1061668
[46]	Kirschstein T, Köhling R (2009) What is the source of the EEG? Clin. EEG Neur 40: 146–149. https://doi.org/10.1177/155005940904000305 doi: 10.1177/155005940904000305
[47]	Knutson B, Genevsky A (2018) Neuroforecasting aggregate choice. Curr Direct Psych Sci 27: 110–115. https://doi.org/10.1177/0963721417737877 doi: 10.1177/0963721417737877
[48]	Kolar T, Batagelj Z, Omeragić I, et al. (2021) How moment-to-moment EEG measures enhance ad effectiveness evaluation: Peak emotions during branding moments as key indicators. J Adv Res 61: 365–381. https://doi.org/10.2501/JAR-2021-014 doi: 10.2501/JAR-2021-014
[49]	Krampe C, Strelow E, Haas A, et al. (2018) The application of mobile fNIRS to "shopper neuroscience"–first insights from a merchandising communication study. Eur J Mark 52: 244–259. https://doi.org/10.1108/EJM-12-2016-0727 doi: 10.1108/EJM-12-2016-0727
[50]	Kumar B, Sharma A, Vatavwala S, et al. (2020) Digital mediation in business-to-business marketing: A bibliometric analysis. Ind Mark Manage 85: 126–140. https://doi.org/10.1016/j.indmarman.2019.10.002 doi: 10.1016/j.indmarman.2019.10.002
[51]	Le TT (2023) The association of corporate social responsibility and sustainable consumption and production patterns: The mediating role of green supply chain management. J Clean Prod 414: 137435. https://doi.org/10.1016/j.jclepro.2023.137435 doi: 10.1016/j.jclepro.2023.137435
[52]	Lee EJ, Kwon G, Shin HJ, et al. (2014) The spell of green: Can frontal EEG activations identify green consumers? J Bus Eth 122: 511–521. https://doi.org/10.1007/s10551-013-1775-2 doi: 10.1007/s10551-013-1775-2
[53]	Lee N, Brandes L, Chamberlain L, et al. (2017).This is your brain on neuromarketing: Reflections on a decade of research. J Mark Manage 33: 878–892. https://doi.org/10.1080/0267257X.2017.1327249 doi: 10.1080/0267257X.2017.1327249
[54]	Lee N, Broderick AJ, Chamberlain L (2007) What is 'neuromarketing'? A discussion and agenda for future research. Int J Psych 63: 199–204. https://doi.org/10.1016/j.ijpsycho.2006.03.007 doi: 10.1016/j.ijpsycho.2006.03.007
[55]	Lee N, Chamberlain L, Brandes L (2018) Welcome to the jungle! The neuromarketing literature through the eyes of a newcomer. Eur J Mark 52: 4–38. https://doi.org/10.1108/EJM-02-2017-0122 doi: 10.1108/EJM-02-2017-0122
[56]	Levallois C, Smidts A, Wouters P (2021) The emergence of neuromarketing investigated through online public communications (2002–2008). Bus Hist 63: 443–466. https://doi.org/10.1080/00076791.2019.1579194 doi: 10.1080/00076791.2019.1579194
[57]	Li SZ, Jain AK, Huang T, et al. (2005) Face recognition applications. Hand Face Recog, 371–390.
[58]	Lim WM (2018) Demystifying neuromarketing. J Bus Res 91: 205–220. https://doi.org/10.1016/j.jbusres.2018.05.036 doi: 10.1016/j.jbusres.2018.05.036
[59]	Lim WM, Yap SF, Makkar M (2021) Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading? J Bus Res 122: 534–566. https://doi.org/10.1016/j.jbusres.2020.08.051 doi: 10.1016/j.jbusres.2020.08.051
[60]	Liu Y, Zhao R, Xiong X, et al. (2023) A Bibliometric analysis of consumer neuroscience towards sustainable consumption. Behav Sci 13: 298–315. https://doi.org/10.3390/bs13040298 doi: 10.3390/bs13040298
[61]	Luna-Nevarez C (2021) Neuromarketing, ethics, and regulation: An exploratory analysis of consumer opinions and sentiment on blogs and social media. J Cons Pol 44: 559–583. https://doi.org/10.1007/s10603-021-09496-y doi: 10.1007/s10603-021-09496-y
[62]	Mariani MM, Al-Sultan K, De Massis A (2023). Corporate social responsibility in family firms: A systematic literature review. J Small Bus Manage 61: 1192–1246. https://doi.org/10.1080/00472778.2021.1955122 doi: 10.1080/00472778.2021.1955122
[63]	Marzouk OA, Mahrous AA (2020) Sustainable consumption behavior of energy and water-efficient products in a resource-constrained environment. J Glob Mark 33: 335–353. https://doi.org/10.1080/08911762.2019.1709005 doi: 10.1080/08911762.2019.1709005
[64]	McDonald MA, Tayebi M, McGeown JP, et al. (2022) A window into eye movement dysfunction following mTBI: a scoping review of magnetic resonance imaging and eye tracking findings. Brain Behav 12: e2714. https://doi.org/10.1002/brb3.2714 doi: 10.1002/brb3.2714
[65]	Meyerding SG, Mehlhose CM (2020) Can neuromarketing add value to the traditional marketing research? An exemplary experiment with functional near-infrared spectroscopy (fNIRS). J Bus Res 107: 172–185. https://doi.org/10.1016/j.jbusres.2018.10.052 doi: 10.1016/j.jbusres.2018.10.052
[66]	Mishra S, Malhotra G, Chatterjee R, et al. (2021) Impact of self-expressiveness and environmental commitment on sustainable consumption behavior: The moderating role of fashion consciousness. J Strat Mark, 1–23. https://doi.org/10.1080/0965254X.2021.1892162 doi: 10.1080/0965254X.2021.1892162
[67]	Motoki K, Sugiura M, Kawashima R (2019). Common neural value representations of hedonic and utilitarian products in the ventral stratum: An fMRI study. Sci Rep 9: 1–10. https://doi.org/10.1038/s41598-019-52159-9 doi: 10.1038/s41598-018-37186-2
[68]	Motoki K, Suzuki S, Kawashima R, et al. (2020) A combination of self-reported data and social-related neural measures forecasts viral marketing success on social media. J Interact Mark 52: 99–117. https://doi.org/10.1016/j.intmar.2020.06.003 doi: 10.1016/j.intmar.2020.06.003
[69]	Murphy ER, Illes J, Reiner PB (2008) Neuroethics of neuromarketing. J Cons Behav An Int Res Rev 7: 293–302. https://doi.org/10.1002/cb.252 doi: 10.1002/cb.252
[70]	Nasir O, Javed RT, Gupta S, et al. (2023) Artificial intelligence and sustainable development goals nexus via four vantage points. Techn Soc 72: 102171. https://doi.org/10.1016/j.techsoc.2022.102171 doi: 10.1016/j.techsoc.2022.102171
[71]	Negrete-Cardoso M, Rosano-Ortega G, Álvarez-Aros EL, et al. (2022) Circular economy strategy and waste management: A bibliometric analysis in its contribution to sustainable development, toward a post-COVID-19 era. Env Sci Poll Res 29: 61729–61746. https://doi.org/10.1007/s11356-022-18703-3 doi: 10.1007/s11356-022-18703-3
[72]	Nemorin S (2017) Neuromarketing and the "poor in world" consumer: how the animalization of thinking underpins contemporary market research discourses. Cons Mark Cult 20: 59–80. https://doi.org/10.1080/10253866.2016.1160897 doi: 10.1080/10253866.2016.1160897
[73]	Nilashi M, Samad S, Ahmadi N, et al. (2020). Neuromarketing: a review of research and implications for marketing. J Soft Comp Dec Supp Sys 7: 23–31.
[74]	Oliveira PM, Guerreiro J, Rita P (2022) Neuroscience research in consumer behavior: A review and future research agenda. Int J Cons Stud 46: 2041–2067. https://doi.org/10.1111/ijcs.12800 doi: 10.1111/ijcs.12800
[75]	Pagan NM, Pagan KM, Teixeira AA, et al. (2020) Application of neuroscience in the area of sustainability: Mapping the territory. Glob J Flex Sys Manage 21: 61–77. https://doi.org/10.1007/s40171-020-00243-9 doi: 10.1007/s40171-020-00243-9
[76]	Paul J, Benito GRG (2018) A review of research on outward foreign direct investment from emerging countries, including China: what do we know, how do we know and where should we be heading? Asia Pac Bus Rev 24: 90–115. https://doi.org/10.1080/13602381.2017.1357316 doi: 10.1080/13602381.2017.1357316
[77]	Paul J, Parthasarathy S, Gupta P (2017) Exporting challenges of SMEs: A review and future research agenda. J World Bus 52: 327–342. https://doi.org/10.1016/j.jwb.2017.01.003 doi: 10.1016/j.jwb.2017.01.003
[78]	Pérez-Martínez J, Hernandez-Gil F, San Miguel G, et al. (2023) Analysing associations between digitalization and the accomplishment of the Sustainable Development Goals. Sci Total Env 857: 159700. https://doi.org/10.1016/j.scitotenv.2022.159700 doi: 10.1016/j.scitotenv.2022.159700
[79]	Piracci G, Casini L, Contini C, et al. (2023) Identifying key attributes in sustainable food choices: An analysis using the food values framework. J Clean Prod 416: 137924. https://doi.org/10.1016/j.jclepro.2023.137924 doi: 10.1016/j.jclepro.2023.137924
[80]	Plassmann H, Venkatraman V, Huettel S, et al. (2015) Consumer neuroscience: applications, challenges, and possible solutions. J Mark Res 52: 427–435.https://doi.org/10.1509/jmr.14.0048 doi: 10.1509/jmr.14.0048
[81]	Powers A, Fani N, Murphy L, et al. (2019) Attention bias toward threatening faces in women with PTSD: Eye tracking correlates by symptom cluster. Eur J Psy 10: 1568133. https://doi.org/10.1080/20008198.2019.1568133 doi: 10.1080/20008198.2019.1568133
[82]	Qananwah Q, Alqudah AM, Alodat MD, et al. (2022) Detecting cognitive features of videos using EEG signal. Comp J 65: 105–123. https://doi.org/10.1093/comjnl/bxaa180 doi: 10.1093/comjnl/bxaa180
[83]	Qasim MS, Bari DS, Martinsen ØG (2022) Influence of ambient temperature on tonic and phasic electrodermal activity components. Phys Meas 43: 065001. https://doi.org/10.1088/1361-6579/ac72f4 doi: 10.1088/1361-6579/ac72f4
[84]	Qazi A, Simsekler MCE, Al-Mhdawi MKS (2023) Exploring network-based dependencies between country-level sustainability and business risks. J Clean Prod 418: 138161. https://doi.org/10.1016/j.jclepro.2023.138161 doi: 10.1016/j.jclepro.2023.138161
[85]	Ramsøy TZ (2019) Building a foundation for neuromarketing and consumer neuroscience research: How researchers can apply academic rigor to the neuroscientific study of advertising effects. J Adv Res 59: 281–294.https://doi.org/10.2501/JAR-2019-034 doi: 10.2501/JAR-2019-034
[86]	Rana IA, Khaled S, Jamshed A, Nawaz A (2022) Social protection in disaster risk reduction and climate change adaptation: A bibliometric and thematic review. J Integ Env Sci 19: 65–83. https://doi.org/10.1080/1943815X.2022.2108458 doi: 10.1080/1943815X.2022.2108458
[87]	Rawnaque FS, Rahman K M, Anwar SF, et al. (2020). Technological advancements and opportunities in neuromarketing: a systematic review. Brain Inf 7: 1–19.https://doi.org/10.1186/s40708-020-00109-x doi: 10.1186/s40708-020-0102-9
[88]	Reilly RG, Peelle JE (2021) The left inferior frontal gyrus is sensitive to individual differences in reading comprehension ability: An eye-tracking study. PLoS One 16: e0245897. https://doi.org/10.1371/journal.pone.0245897 doi: 10.1371/journal.pone.0245897
[89]	Savelli E, Gregory‐Smith D, Murmura F, et al. (2022) How to communicate typical–local foods to improve food tourism attractiveness. Psy. Mark 39: 1350–1369. https://doi.org/10.1002/mar.21668 doi: 10.1002/mar.21668
[90]	Shen F, Morris JD (2016) Decoding neural responses to emotion in television commercials: an integrative study of self-reporting and fMRI measures. J Adv Res 56: 193–204. https://doi.org/10.2501/JAR-2016-016 doi: 10.2501/JAR-2016-016
[91]	Singh J, Goyal G, Gill R (2020) Use of neurometrics to choose optimal advertisement method for omnichannel business. Ent Inf Sys 14: 243–265. https://doi.org/10.1080/17517575.2019.1640392 doi: 10.1080/17517575.2019.1640392
[92]	Silva M (2015) A systematic review of Foresight in Project Management literature. Proc Comp Sci 64: 792–799. https://doi.org/10.1016/j.procs.2015.08.630 doi: 10.1016/j.procs.2015.08.630
[93]	Smidts A, Hsu M, Sanfey AG, et al. (2014) Advancing consumer neuroscience. Mark Lett 25: 257–267. https://doi.org/10.1007/s11002-014-9306-1 doi: 10.1007/s11002-014-9306-1
[94]	Solnais C, Andreu-Perez J, Sánchez-Fernández J, et al. (2013) The contribution of neuroscience to consumer research: A conceptual framework and empirical review. J Econ Psy 36: 68–81. https://doi.org/10.1016/j.joep.2013.02.011 doi: 10.1016/j.joep.2013.02.011
[95]	Stanton SJ, Sinnott-Armstrong W, Huettel SA (2017) Neuromarketing: Ethical implications of its use and potential misuse. J Bus Eth 144: 799–811. https://doi.org/10.1007/s10551-016-3059-0 doi: 10.1007/s10551-016-3059-0
[96]	Stillman P, Lee H, Deng X, et al. (2020) Examining consumers' sensory experiences with color: A consumer neuroscience approach. Psy Mark 37: 995–1007. https://doi.org/10.1002/mar.21360 doi: 10.1002/mar.21360
[97]	Varan D, Lang A, Barwise P, et al. (2015) How reliable are neuromarketers' measures of advertising effectiveness? Data from ongoing research holds no common truth among vendors. J Adv Res 55: 176–191. https://doi.org/10.2501/JAR-55-2-176-191 doi: 10.2501/JAR-55-2-176-191
[98]	Vyas S, Seal A (2022) A deep convolution neural networks framework for analyzing electroencephalography signals in neuromarketing, In: Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021. Singapore: Springer Nature Singapore, 119–127. https://doi.org/10.1007/978-981-19-0105-8_12
[99]	Wajid A, Raziq MM, Ahmed QM, et al. (2021) Observing viewers' self-reported and neurophysiological responses to message appeal in social media advertisements. J Retail Cons Serv 59: 102373. https://doi.org/10.1016/j.jretconser.2020.102373 doi: 10.1016/j.jretconser.2020.102373
[100]	Wei Q, Lv D, Lin Y, et al. (2023) Influence of utilitarian and hedonic attributes on willingness to pay green product premiums and neural mechanisms in China: An ERP study. Sustainability 15: 2403. https://doi.org/10.3390/su15032403 doi: 10.3390/su15032403
[101]	WTO (2023) Available from: https://www.wto.org/english/forums_e/public_forum23_e/public_forum23_e.htm
[102]	Xu Z, Wang X, Wang X, et al. (2021) A comprehensive bibliometric analysis of entrepreneurship and crisis literature published from 1984 to 2020. J Bus Res 135: 304–318. https://doi.org/10.1016/j.jbusres.2021.06.051 doi: 10.1016/j.jbusres.2021.06.051
[103]	Yuan J, Zhang Y, Zhao Y, et al. (2023) The emotion-regulation benefits of implicit reappraisal in clinical depression: Behavioral and electrophysiological evidence. Neur Bull 39: 973–983. https://doi.org/10.1007/s12264-022-00973-z doi: 10.1007/s12264-022-00973-z
[104]	Zhang Y, Bian Y, Wu H, et al. (2023) Intuition or rationality: Impact of critical thinking dispositions on the cognitive processing of creative information. Thin Skills Create 48: 101278. https://doi.org/10.1016/j.tsc.2023.101278 doi: 10.1016/j.tsc.2023.101278
[105]	Zhao M (2022) The impact of cognitive conflict on product-service system value cocreation: An event-related potential perspective. J Clean Prod 331: 129987. https://doi.org/10.1016/j.jclepro.2021.129987 doi: 10.1016/j.jclepro.2021.129987
[106]	Zhu Z, Jin Y, Su Y, et al. (2022). Evaluation of the neuromarketing research trend: 2010–2021. Front Psyc 13: 872468. https://doi.org/10.3389/fpsyg.2022.872468 doi: 10.3389/fpsyg.2022.872468

This article has been cited by:

1.	Young Jin Lee, Joong Seob Lee, Olatunji Ajiteru, Ok Joo Lee, Ji Seung Lee, Hanna Lee, Seong Wan Kim, Jong Woo Park, Kee Young Kim, Kyu Young Choi, Heesun Hong, Tipu Sultan, Soon Hee Kim, Chan Hum Park, Biocompatible fluorescent silk fibroin bioink for digital light processing 3D printing, 2022, 213, 01418130, 317, 10.1016/j.ijbiomac.2022.05.123
2.	Shotaro Namba, Hisaaki Kato, Shuji Shigenobu, Takashi Makino, Hisao Moriya, C Hoffman, Massive expression of cysteine-containing proteins causes abnormal elongation of yeast cells by perturbing the proteasome, 2022, 12, 2160-1836, 10.1093/g3journal/jkac106
3.	Andreea Veronica Dediu Botezatu, Gabriela Elena Bahrim, Claudia Veronica Ungureanu, Anna Cazanevscaia Busuioc, Bianca Furdui, Rodica Mihaela Dinica, Green “one-pot” fluorescent bis-indolizine synthesis with whole-cell plant biocatalysis, 2023, 12, 2191-9550, 10.1515/gps-2023-0046
4.	Belen Torrado, Bruno Pannunzio, Leonel Malacrida, Michelle A. Digman, Fluorescence lifetime imaging microscopy, 2024, 4, 2662-8449, 10.1038/s43586-024-00358-8

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)